[1]} first proposed a truly joint extraction model for this task. They used a sequence tagging scheme to jointly extract the entities and relations. They created a set of tags derived from the Cartesian product of entity tags and relation tags. These new tags can encode the entity information and relation information together. But this strategy does not work when entities are shared among multiple tuples, as only one tag can be assigned to a token. [2]} proposed an encoder-decoder model with a copy mechanism to extract relation tuples with overlapping entities. Their model has a copy network to copy the last token of two entities from the source sentence and a classification network to classify the relation between copied tokens. Their model cannot extract the full entity names of the tuples. Their best performing model uses a separate decoder to extract each tuple. During training, they need to fix the maximum number of decoders and during inference, their model can only extract up to that fixed number of tuples. Also, due to the use of separate decoders for each tuple, their model misses the interaction among the tuples.
